When | Where | Start | Lecturer |
---|---|---|---|

Monday, 12:15-13:45 | Friedrich-Hirzebruch Allee 5 - Hörsaal 3 | October 7 | Kesselheim |

Wednesday, 12:15-13:45 | Friedrich-Hirzebruch Allee 5 - Hörsaal 3 | October 9 | Kesselheim |

In many application scenarios, algorithms have to make decisions under some kind of uncertainty. This affects different kinds of problems. For example, when planing a route, a navigation system should take into consideration the traffic. Also, any machine-learning problem is about some kind of uncertainty. A random sample of data is used as a representative for the entire world.

In this course, we will get to know different techniques to model uncertainty and what approaches algorithms can use to cope with it. We will cover topics such as

- Online Algorithms
- Online Learning Algorithms and Online Convex Optimization
- Markov Decision Processes
- Stochastic and Robust Optimization

You should bring a solid background in algorithms, calculus, and probability theory. Specialized knowledge about certain algorithms is not necessary.

There is a requirement for participating in the exams. Once during the semester, you need to present the solution of a homework problem in one of the tutorials. If you would like to present a solution, please send an email to rlehming(at)uni-bonn(dot)de until Monday at the latest. If multiple people want to do the same exercise, it is first come, first serve. Before you present it to everyone, we will schedule a short meeting (10-15min) for a quick discussion of your solution.